The specification of pending patent application Ser. No. 11/126,518, filed May 10, 2005, is hereby incorporated by reference in its entirety for its teaching (herein referred to as “the referenced Ser. No. 11/126,518”).
Wind tunnel tests can be conducted utilizing phased microphone arrays. A phased microphone array is typically configured as a group of microphones arranged in an optimized pattern. The signals from each microphone can be sampled and then processed in the frequency domain. The relative phase differences seen at each microphone determines where noise sources are located. The amplification capability of the array allows detection of noise sources well below the background noise level. This makes microphone arrays particularly useful for wind tunnel evaluations of airframe noise since, in most cases, the noise produced by wings, flaps, struts and landing gear models will be lower than that of the wind tunnel environment.
The use of phased arrays of microphones in the study of aeroacoustic sources has increased significantly in recent years, particularly since the mid 1990's. The popularity of phased arrays is due in large part to the apparent clarity of array-processed results, which can reveal noise source distributions associated with, for example, wind tunnel models, and full-scale aircraft. Properly utilized, such arrays are powerful tools that can extract noise source radiation information in circumstances where other measurement techniques may fail. Presentations of array measurements of aeroacoustic noise sources, however, can lend themselves to a great deal of uncertainty during interpretation. Proper interpretation requires knowledge of the principles of phased arrays and processing methodology. Even then, because of the complexity, misinterpretations of actual source distributions (and subsequent misdirection of engineering efforts) are highly likely.
Prior to the mid 1980's, processing of array microphone signals as a result of aeroacoustic studies involved time delay shifting of signals and summing in order to strengthen contributions from, and thus “focus” on, chosen locations over surfaces or positions in the flow field. Over the years, with great advances in computers, this basic “delay and sum” processing approach has been replaced by “classical beamforming” approaches involving spectral processing to form cross spectral matrices (CSM) and phase shifting using increasingly large array element numbers. Such advances have greatly increased productivity and processing flexibility, but have not changed at all the interpretation complexity of the processed array results.
Some aeroacoustic testing has involved the goal of forming a quantitative definition of different airframe noise sources spectra and directivity. Such a goal has been achieved with arrays in a rather straight-forward manner for the localized intense source of flap edge noise. For precise source localization, however, Coherent Output Power (COP) methods can be utilized by incorporating unsteady surface pressure measurements along with the array. Quantitative measurements for distributed sources of slat noise have been achieved utilizing an array and specially tailored weighting functions that matched array beam patterns with knowledge of the line source type distribution for slat noise. Similar measurements for distributed trailing edge noise and leading edge noise (e.g., due in this case to grit boundary layer tripping) have bee performed along with special COP methodologies involving microphone groups.
The deconvolution methodology described in the referenced Ser. No. 11/126,518 gives a unique robust deconvolution approach designed to determine the “true” noise source distribution over an aeroacoustic source region to replace the “classical beamformed” distributions. However, that method, along with classical beamforming processing, employs statistically independent (incoherent) noise source distribution assumptions. Thus, it can produce results that are inaccurate and distorted in the presence of coherent sources, albeit a suitable solution for where non-coherent sources are involved. Using an equation form similar to that employed in the referenced Ser. No. 11/126,518, a solution appropriate to identify and quantify coherent as well as an incoherent sources is viable and will be herein fully described.
Example applications for the present invention include ideal point and line noise source cases, as well as conformation with well documented experimental airframe noise studies of wing trailing and leading edge noise, slat noise, and flap edge/flap cove noise.